macOS Junk Cleaner
A Python-based CLI utility to scan and remove macOS-specific junk files (e.g., .DS_Store, ._*, .Spotlight-V100) from directories.
Installation
Using pip
pip install .
Using uv
uv tool install .
Usage
Scan
To see what would be cleaned with a detailed summary:
mac-clean scan /path/to/dir
# or using uv
uv run mac-clean scan /path/to/dir
Clean
The clean command is used for removal. For safety, it defaults to dry-run mode.
# Preview deletion (Dry-run)
mac-clean clean /path/to/dir
# Perform actual deletion
mac-clean clean /path/to/dir --force
Development
Setup (uv)
- Clone the repository.
- Sync the environment (creates venv and installs all dependencies and dev tools):
uv sync - Install pre-commit hooks:
uv run pre-commit install
Linting and Formatting
This project uses Ruff for linting and formatting.
# Lint
ruff check .
# or using uv
uv run ruff check .
# Format
ruff format .
# or using uv
uv run ruff format .
Testing
pytest
# or using uv
uv run pytest
License
This project is licensed under the MIT License - see the LICENSE file for details.
1 maintainer
Metadata
1 maintainer
Release files for macos-junk-cleaner 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| macos_junk_cleaner-1.0.0.tar.gz | 6.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| macos_junk_cleaner-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.6 kB
Release files / macos_junk_cleaner-1.0.0.tar.gz
| Download URL | macos_junk_cleaner-1.0.0.tar.gz |
|---|---|
| Size | 6.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / macos_junk_cleaner-1.0.0-py3-none-any.whl
| Download URL | macos_junk_cleaner-1.0.0-py3-none-any.whl |
|---|---|
| Size | 6.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
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PyPI Publish Attestation
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